We iteratively chose the extracted curb point clouds and applied unsupervised spatial clustering. The boundary points of a cluster are the points which are furthest from the cluster centroids.
If the \(L_2\) -norm of the boundary points in the new clusters were less than a pre-defined threshold from the old cluster boundary points we merged the clusters. This operation helped in identifying the number of curb segments. The result of our clustering with DBSCAN [1]} is shown in fig:curb-extraction.
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